Prediction of Platinum Prices Using Dynamically Weighted Mixture of Experts
Baruch Lubinsky; Bekir Genc; Tshilidzi Marwala · 2008 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Neural networks are powerful tools for classification and regression in static environments. This paper describes a technique for creating an ensemble of neural networks that adapts dynamically to changing conditions. The model separates the input space into four regions and each network is given a weight in each region based on its performance on samples from that region. The ensemble adapts dynamically by constantly adjusting these weights based on the current performance of the networks. The data set used is a collection of financial indicators with the goal of predicting the future platinu
Excerpt shown for reference under fair use — read the full paper at the publisher.
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Metadata source: arXiv